Condition monitoring is the process of determining the condition of machinery while in operation. Condition monitoring enables the repair of problem components prior to failure and not only helps plant personnel reduce the possibility of catastrophic failure, but also allows them to order parts in advance, schedule manpower, and plan other repairs during downtime.
Components such as rolling-element bearings are often used in critical applications, wherein their failure in service would result in significant commercial loss to the end-user. It is therefore important to be able to predict the residual life of such a bearing, in order to plan intervention in a way that avoids failure in service, while minimizing the losses that may arise from taking the machinery in question out of service to replace the bearing.
The residual life of a rolling-element bearing is generally determined by fatigue of the operating surfaces as a result of repeated stresses in operational use. Fatigue failure of a rolling element bearing results from progressive flaking or pitting of the surfaces of the rolling elements and of the surfaces of the corresponding bearing races. The flaking and pitting may cause seizure of one or more of the rolling elements, which in turn may generate excessive heat, pressure and friction.
Bearings are selected for a specific application on the basis of a calculated or predicted residual life expectancy compatible with the expected type of service in the application in which they will be used. However, this type of life prediction is considered inadequate for the purpose of maintenance planning for several reasons.
One reason is that the actual operation conditions may be quite different from the nominal conditions. Another reason is that a bearing's residual life may be radically compromised by short-duration events or unplanned events, such as overloads, lubrication failures, installation errors, etc. Yet another reason is that, even if nominal operating conditions are accurately reproduced in service, the inherently random character of the fatigue process may give rise to large statistical variations in the actual residual life of substantially identical bearings.
In order to improve maintenance planning, it is common practice to monitor the values of physical quantities related to vibrations and temperature to which a component, such as a bearing, is subjected in operational use, so as to be able to detect the first signs of impending failure.
In a condition monitoring system data dynamic signal data is obtained in the form of a time waveform (i.e. a graph of a varying quantity against time which usually consists of many samples) and/or a Fast Fourier Transform (FFT) from at least one sensor. These time waveforms and/or FFTs are usually transmitted and displayed to an analyst. This can however result in long transmission and display times and the data can be difficult to display or interpret. The transmission, display and interpretation of such data can require a significant amount of energy, time and expertise, and consequently decreases the rate at which measurements and analyses can be made.
There are condition monitoring systems using vibration level sensors in which only the “overall amplitudes” (i.e. the total amount of vibration occurring in a selected frequency range) of a particular signal are transmitted or displayed. However, such transmitted or displayed data has limited value since it provides no information about the nature of a signal. Additionally, no further information can be post-processed from the transmitted or displayed overall amplitude values.